Gendered Self-Views Across 62 Countries: A Test of Competing Models

Abstract

Social role theory posits that binary gender gaps in agency and communion should be larger in less egalitarian countries, reflecting these countries’ more pronounced sex-based power divisions. Conversely, evolutionary and self-construal theorists suggest that gender gaps in agency and communion should be larger in more egalitarian countries, reflecting the greater autonomy support and flexible self-construction processes present in these countries. Using data from 62 countries (N = 28,640), we examine binary gender gaps in agentic and communal self-views as a function of country-level objective gender equality (the Global Gender Gap Index) and subjective distributions of social power (the Power Distance Index). Findings show that in more egalitarian countries, gender gaps in agency are smaller and gender gaps in communality are larger. These patterns are driven primarily by cross-country differences in men’s self-views and by the Power Distance Index (PDI) more robustly than the Global Gender Gap Index (GGGI). We consider possible causes and implications of these findings.

Full text

Running head: Gendered Self-Views across 62 Countries
Gendered Self-Views Across 62 Countries: A Test of Competing
Models
Natasza Kosakowska-Berezecka1, Jennifer K. Bosson2, Paweł Jurek1, Tomasz Besta1, Michał
Olech3, Joseph A. Vandello2, Michael Bender4, Justine Dandy5, Vera Hoorens6, Inga
Jasinskaja-Lahti7, Eric Mankowski8, Satu Venäläinen7, Sami Abuhamdeh9, Collins Badu
Agyemang10, Gülçin Akbaş11, Nihan Albayrak-Aydemir12,13, Soline Ammirati14, Joel
Anderson15,16, Gulnaz Anjum17, Amarina Ariyanto18, John Jamir Benzon R. Aruta19,20,
Mujeeba Ashraf21, Aistė Bakaitytė22, Maja Becker23, Chiara Bertolli24, Dashamir Bërxulli25,
Deborah L. Best26, Chongzeng Bi27, Katharina Block28, Mandy Boehnke29, Renata
Bongiorno30, Janine Bosak31, Annalisa Casini32, Qingwei Chen33, Peilian Chi34, Vera Cubela
Adoric35, Serena Daalmans36, Soledad de Lemus37, Sandesh Dhakal38, Nikolay
Dvorianchikov39, Sonoko Egami40, Edgardo Etchezahar41, Carla Sofia Esteves42, Laura
Froehlich43, Efrain Garcia- Sanchez37, Alin Gavreliuc44, Dana Gavreliuc44, Ángel Gomez45,
Francesca Guizzo46, Sylvie Graf47, Hedy Greijdanus48, Ani Grigoryan49, Joanna Grzymała-
Moszczyńska50, Keltouma Guerch51,52, Marie Gustafsson Sendén53, Miriam-Linnea Hale54,
Hannah Hämer55, Mika Hirai56, Lam Hoang Duc57, Martina Hřebíčková47, Paul B.
Hutchings58, Dorthe Høj Jensen59, Serdar Karabati60, Kaltrina Kelmendi25, Gabriella
Kengyel61, Narine Khachatryan49, Rawan Ghazzawi4, Mary Kinahan62, Teri A. Kirby63,
Monika Kovacs64, Desiree Kozlowski65, Vladislav Krivoshchekov66, Kuba Kryś67, Clara
Kulich68, Tai Kurosawa69, Nhan Thi Lac An70, Javier Labarthe-Carrara71, Mary Anne Lauri72,
Ioana Latu73, Abiodun Musbau Lawal74, Junyi Li75, Jana Lindner76, Anna Lindqvist77, Angela
T. Maitner78, Elena Makarova76, Ana Makashvili79, Shera Malayeri66, Sadia Malik80, Tiziana
Mancini81, Claudia Manzi82, Silvia Mari83, Sarah E. Martiny84, Claude-Hélène Mayer85,
Vladimir Mihić86, Jasna Milošević Đorđević87, Eva Moreno-Bella88, Silvia Moscatelli89,
Andrew Bryan Moynihan90, Dominique Muller91, Erita Narhetali92, Félix Neto93, Kimberly A.
Noels94, Boglárka Nyúl64, Emma C. O’Connor8, Danielle P. Ochoa95, Sachiko Ohno96,
Sulaiman Olanrewaju Adebayo97, Randall Osborne98, Maria Giuseppina Pacilli99, Jorge
Palacio100, Snigdha Patnaik101, Vassilis Pavlopoulos102, Pablo Pérez de León71, Ivana
Piterová103, Juliana Barreiros Porto55, Angelica Puzio125, Joanna Pyrkosz-Pacyna105, Erico
Rentería Pérez106, Emma Renström107, Tiphaine Rousseaux23, Michelle K. Ryan48,108, Saba
Safdar109, Mario Sainz110, Marco Salvati111, Adil Samekin112, Simon Schindler113, A. Timur
Sevincer114, Masoumeh Seydi66, Debra Shepherd115, Sara Sherbaji78,116, Toni Schmader117,
Cláudia Simão118, Rosita Sobhie119, Jurand Sobiecki1, Lucille De Souza117, Emma Sarter33,
Dijana Sulejmanović120, Katie E. Sullivan58, Mariko Tatsumi121, Lucy Tavitian-Elmadjian122,
Suparna Jain Thakur123, Quang Thi Mong Chi57, Beatriz Torre95, Ana Torres124, Claudio V.
Torres55, Beril Türkoğlu126, Joaquín Ungaretti41, Timothy Valshtein104, Colette Van Laar6,
Jolanda van der Noll43, Vadym Vasiutynskyi127, Christin-Melanie Vauclair128, Neharika
Vohra129, Marta Walentynowicz32, Colleen Ward130, Anna Włodarczyk131, Yaping Yang132,
Vincent Yzerbyt32, Valeska Zanello55, Antonella Ludmila Zapata-Calvente37, Magdalena
Zawisza133, Rita Žukauskienė22, Magdalena Żadkowska1

1University of Gdańsk, Poland
2University of South Florida, Tampa, USA
3Medical University of Gdan´sk, Poland
4Tilburg University, The Netherlands
5Edith Cowan University, Joondalup, Western Australia, Australia
6University of Leuven, Belgium
7University of Helsinki, Finland
8Portland State University, OR, USA
9Istanbul Sehir University, Istanbul, Turkey
10University of Ghana, Accra, Ghana
11Atilim University, Ankara, Turkey
12London School of Economics and Political Science, UK
13The Open University, Milton Keynes, UK
14Université Grenoble Alpes, France
15Australian Catholic University, Brisbane, Queensland, Australia
16La Trobe University, Melbourne, Victoria, Australia
17University of Oslo, Norway
18University of Indonesia, Depok, Indonesia
19De La Salle University, Manila, Philippines
20Sunway University, Malaysia
21University of the Punjab, Lahore, Pakistan
22Mykolas Romeris University, Vilnius, Lithuania
23CLLE, Université de Toulouse, France
24University of Padova, Italy
25University of Prishtina, Kosovo
26Wake Forest University, Winston-Salem, NC, USA
27Southwest University, El Paso, TX, USA
28University of Amsterdam, The Netherlands
29University of Bremen, Germany
30University of Exeter, UK
31Business School Dublin City University, Ireland
32Université Catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
33South China Normal University, Guangzhou, China
34University of Macau, China
35University of Zadar, Croatia
36Radboud University, Nijmegen, The Netherlands
37University of Granada, Spain
38Tribhuvan University, Kirtipur, Nepal
39Moscow State University of Psychology and Education, Russia
40Shiraume Gakuen University, Kodaira, Japan
41University of Buenos Aires, Argentina
42Universidade Católica Portuguesa, Católica Lisbon School of Business and
Economics, Portugal
43Fern Universität in Hagen, Germany
44West University of Timisoara, Romania
45Universidad Nacional de Educación a Distancia, Madrid, Spain
46University of Surrey, Guildford, UK
47Czech Academy of Sciences, Prague, Czech Republic
48University of Groningen, The Netherlands

49Yerevan State University, Armenia
50Jagiellonian University, Kraków, Poland
51Mohamed I University, Oujda, Morocco
52Centre Régional des Métiers de l’Education et de la Formation de l’Oriental, Oujda,
Morocco
53Stockholm University, Sweden
54University of Luxembourg, Esch-sur-Alzette, Luxembourg
55University of Brasília, Brazil
56Yokohama City University, Japan
57Vietnam National University Ho Chi Minh City, Vietnam
58University of Wales Trinity Saint David, Lampeter, UK
59Aarhus University, Denmark
60Istanbul Bilgi University, Turkey
61Pazmany Peter Catholic University, Budapest, Hungary
62Technological University Dublin, Ireland
63Purde University, West Lafayette, IN, USA
64Eötvös Loránd University, Budapest, Hungary
65Southern Cross University, Lismore, New South Wales, Australia
66University of Bern, Switzerland
67Institute of Psychology of the Polish Academy of Sciences, Poland
68University of Geneva, Switzerland
69Ibaraki Christian University, Hitachi, Japan
70University of Social Sciences and Humanities, Ho Chi Minh City, Vietnam
71Universidad Cato´lica del Uruguay, Montevideo, Uruguay
72University of Malta, Msida, Malta
73Queen’s University Belfast, UK
74Federal University Oye-Ekiti, Nigeria
75Central China Normal University, Wuhan, China
76University of Basel, Switzerland
77Lund University, Sweden
78American University of Sharjah, United Arab Emirates
79Ilia State University, Tbilisi, Georgia
80University of Sargodha, Pakistan
81University of Parma, Italy
82Catholic University of Milan, Italy
83University of Milano-Bicocca, Italy
84UiT The Arctic University of Norway, Tromsø, Norway
85University of Johannesburg, South Africa
86University of Novi Sad, Serbia
87Singidunum University, Belgrade, Serbia
88University of Cordoba, Spain
89University of Bologna, Italy
90University of Limerick, Ireland
91Universite´ Grenoble Alpes, France
92Universitas Indonesia, Depok, Indonesia
93Universidade do Porto, Portugal
94University of Alberta, Edmonton, Canada
95University of the Philippines Diliman, Quezon City, Philippines
96Shirayuri University, Chofu, Japan

97Ekiti State University, Ado Ekiti, Nigeria
98Texas State University, San Marcos, USA
99University of Perugia, Italy
100Universidad del Norte, Colombia, Barranquilla, Colombia
101Xavier University of Bhubaneswar, India
102National and Kapodistrian University of Athens, Greece
103Slovak Academy of Sciences, Bratislava, Slovakia
104Harvard University, USA
105AGH University of Science and Technology, Krako´w, Poland
106University of Valle, Cali, Colombia
107Kristianstad University, Sweden
108The Australian National University, Australia
109University of Guelph, Ontario, Canada
110Pontificia Universidad Católica de Chile, Santiago, Chile
111University of Verona, Italy
112School of Liberal Arts, M. Narikbayev KAZGUU University, Astana, Kazakhstan
113University of Kassel, Germany
114University of Hamburg, Germany
115Stellenbosch University, South Africa
116University College London, UK
117The University of British Columbia, Vancouver, Canada
118Católica Lisbon School of Business & Economics, Portugal
119Universiteit van Suriname, Paramaribo, Suriname
120University of Bihac, Bosnia and Herzegovina
121Osaka Metropolitan University, Japan
122Haigazian University, Beirut, Lebanon
123University of Delhi, New Delhi, India
124Federal University of Paraíba, João Pessoa, Brazil
125New York University, USA
126TED University, Turkey
127National Academy of Educational Sciences of Ukraine, Kyiv, Ukraine
128Instituto Universitário de Lisboa (ISCTE-IUL), CIS, Lisboa, Portugal
129Indian Institute of Management, Ahmedabad, India
130Victoria University of Wellington, New Zealand
131Universidad Católica del Norte, Antofagasta, Chile
132Ningbo University, China
133Anglia Ruskin University, Cambridge, UK
This research was funded by a grant from the National Science Centre in Poland
(grant number: 2017/26/M/HS6/00360) awarded to Natasza Kosakowska-Berezecka. Data
collection by the following researchers was supported by grants as follows: Emma C.
O'Connor (grant RL5GM118963 from National Institute of General Medical Sciences of the
National Institutes of Health); Angel Gomez (grant RTI2018-093550-B-I00 from the
Universidad Nacional de Educación a Distancia, Spain); Sylvie Graf and Martina Hřebíčková
(grant 20-01214S from the Czech Science Foundation, and grant RVO: 68081740 from the
Institute of Psychology, Czech Academy of Sciences); Teri A. Kirby (grant ES/S00274X/1
from the Economic and Social Research Council); Soledad de Lemus (PID2019-111549GB-
I00 funded by MCIN/AEI/10.13039/501100011033); Michelle K. Ryan and Renata
Bongiorno (grant ERC-2016-COG 725128 from the European Research Council awarded to

Michelle K. Ryan); Marie Gustafsson Sendén, Anna Lindqvist, and Emma Renström (grant
2017-00414 from the Swedish Research Council for Health, Working Life, and Welfare);
Claudio V. Torres (grant DPI / DIRPE n. 04/2019 from the University of Brasilia).
The results presented in this paper are part of the larger project entitled “Towards
Gender Harmony” (www.towardsgenderharmony), which involves many wonderful people.
Here, we acknowledge our University of Gdańsk Research Assistants Team: Agata Bizewska,
Mariya Amiroslanova, Aleksandra Głobińska, Andy Milewski, Piotr Piotrowski, Stanislav
Romanov, Aleksandra Szulc, and Olga Żychlińska for their assistance with programming the
surveys and coordinating the collection of data at all sites.

Abstract
Social role theory posits that binary gender gaps in agency and communion should be larger
in less egalitarian countries, reflecting these countries’ more pronounced sex-based power
divisions. Conversely, evolutionary and self-construal theorists suggest that gender gaps in
agency and communion should be larger in more egalitarian countries, reflecting the greater
autonomy support and flexible self-construction processes present in these countries. Using
data from 62 countries (N = 28,640) we examine binary gender gaps in agentic and
communal self-views as a function of country-level objective gender equality (the Global
Gender Gap Index) and subjective distributions of social power (the Power Distance Index).
Findings show that in more egalitarian countries, gender gaps in agency are smaller, and
gender gaps in communality are larger. These patterns are driven primarily by cross-country
differences in men’s self-views, and by the PDI more robustly than the GGGI. We consider
possible causes and implications of these findings.
Keywords: communality, agency, self-views, binary sex differences, egalitarianism, gender
equality

Gendered Self-Views Across 62 Countries: A Test of Competing Models
How do women’s and men’s gendered self-views differ across cultures? Different
perspectives offer competing answers to this question. On the one hand, social role theory
(Eagly & Steffen, 1984; Wood & Eagly, 2012) posits that binary gender gaps in self-views
should be larger in less egalitarian countries, reflecting these countries’ more pronounced
vertical and horizontal gender segregation of occupational and social roles (Eagly et al.,
2020). On the other hand, evolutionary theorists (cf. Schmitt et al., 2017) and self-construal
theorists (Guimond et al., 2007) suggest gender gaps in gendered self-views should be larger
in more egalitarian countries, reflecting the greater autonomy and flexible self-construction
processes enjoyed in these countries. Here, using data from 62 countries (N = 28,640), we test
these competing hypotheses by examining how binary gender gaps in communal and agentic
self-views vary with both objective and subjective country-level measures of egalitarianism
(the Global Gender Gap Index [GGGI; World Economic Forum, 2020], and the Power
Distance Index [PDI; Hofstede, 2010]).
Explaining Gendered Self-Views
Communality and agency are dimensions of human evaluation (cf. Bakan, 1966;
Fiske et al., 2002) underlying gender stereotypes and gendered self-views. Stereotypes
linking communality to women and agency to men are cross-culturally universal (Bosson et
al., 2022; Williams & Best, 1990), as are gender gaps in gendered self-views: Across
cultures, women generally rate themselves higher in communal traits than men, and men
generally rate themselves higher in agentic traits than women (Williams & Best, 1990). This
likely occurs because people derive self-views, in part, by internalizing qualities associated
with valued social groups (Tobin et al., 2010; Turner et al., 1987).
And yet, there are individual and cultural differences in the extent to which people
internalize gender stereotypes as stable self-views (Biernat et al., 1996; Wood & Eagly,

2012). Of interest here, cultural factors related to egalitarianism are theorized to covary with
the size of gender gaps in communal and agentic self-views.
Social Role Theory
According to social role theory (Eagly & Steffen, 1984) and its updates (i.e., biosocial
construction theory; Wood & Eagly, 2012), gender gaps in self-views stem distally from sex-
based power and labor divisions, mediated through gender socialization processes. To the
extent that cultures divide power and labor along gender lines, they should more assiduously
socialize girls and boys to adopt traits and preferences that will prepare them for sex-based
roles. For example, in contexts that segregate women and men into non-overlapping domestic
and breadwinner roles, respectively, girls are socialized to be more communal, and boys to be
more agentic. More rigid gender socialization, in turn, encourages internalization of gendered
tendencies, producing larger gender gaps in gendered self-views.
Two types of gender segregation may distally drive gender gaps in self-views.
Whereas vertical segregation is the underrepresentation of women in powerful and high-
status roles, horizontal segregation is the clustering of women and men in occupations of
similar status but differing demands (Charles, 1992; Wong & Charles, 2020). Importantly,
both vertical and horizontal segregation should drive gender gaps in self-views insofar as
both concentrate men in roles requiring agency and competitiveness and women in roles
requiring communality and social skills (Croft et al., 2015; Eagly et al., 2020). Here,
however, we focus exclusively on vertical segregation as a predictor, because this type of
segregation is captured by country-level indicators of gender equality – such as the GGGI –
via measures of women’s economic participation and political empowerment (World
Economic Forum, 2020). Specifically, because countries lower in gender equality tend to
have more traditional sex-based labor divisions (Glick et al., 2000; Wood & Eagly, 2012), we
should observe larger gender gaps in gendered self-views in these countries.

Note that this logic may pertain more to agentic than communal self-views (Eagly et
al., 2020). In less vertically gender segregated countries, women and men are more equally
distributed across high-status roles, which should result in more similar self-views on the
agentic traits predictive of success in such roles. In contrast, even in the most gender equal
countries, women remain visibly overrepresented in the domestic and caretaking roles that
presumably foster communal self-views (e.g., Charmes, 2019). As such, gender gaps in
communal self-views may associate relatively weakly with country-level gender equality.
Supporting social role approaches, increases in gender equality are associated with
smaller gender gaps in self-views (Donnelly & Twenge, 2017), job attribute preferences
(Konrad et al., 2000), sociosexual tendencies (Schmitt, 2005), and mate preferences (Eagly &
Wood, 1999; Zentner & Mitura, 2012).
Evolutionary Theories
According to evolutionary theorists (Buss & Schmitt, 1995; Schmitt, 2015), women
and men evolved different traits and preferences to solve different adaptive problems in
humans’ ancestral past. For instance, gender gaps in parental investment (Trivers, 1972)
presumably created sexual selection pressures that shaped men’s innately higher levels of
agentic traits and women’s innately higher levels of communal traits (Buss, 1997). Although
such gender gaps are universally observed, cultural contexts influence how freely these innate
tendencies can be expressed. Presumably, contemporary environments that more closely
match the hunter-gatherer environments of early humans should best allow adaptive, innate
sex differences to emerge, whereas those that differ markedly from ancestral environments
may impede the emergence of evolved sex differences (e.g., Crawford, 1998). Interestingly,
some propose that more developed countries – as opposed to more agricultural countries –
offer autonomy-supportive ecological and psychological conditions that more closely mimic
humans’ ancestral environments (Schmitt, 2005). Thus, according to some evolutionary

approaches, we should see larger gender gaps in gendered self-views in more egalitarian
countries, as these countries better allow the autonomous expression of women’s and men’s
innate psychological tendencies (Schmitt et al., 2008).
Consistent with this perspective, greater gender equality across cultures is associated
with larger gender gaps in personality traits (Costa et al., 2001; Schmitt et al., 2008),
behavior preferences (Falk & Hermle, 2018), emotional reactions (Niedenthal et al., 2006),
and academic STEM strengths (Stoet & Geary, 2019).
Self-Construal Theories
Combining ideas from social comparison and self-categorization (Turner et al., 1987)
theories, the self-construal approach proposes that people acquire self-views via social
comparisons to others. However, the groups against whom people compare (e.g., own gender
versus other gender) should influence their resulting self-views (Guimond et al., 2007;
Guimond et al., 2010). Moreover, the comparison group or standard that people use when
reporting their self-views varies with countries’ levels of egalitarianism – and more
specifically, power distance. In countries higher in power distance (which are less
egalitarian), people tend to view intergroup boundaries as stable and impermeable, and they
accept hierarchies as legitimate and inevitable; in such countries, people are unlikely to
derive self-views from other-gender social comparisons. Conversely, in countries lower in
power distance, people tend to reject hierarchies and social inequities; in such countries,
gendered self-views more likely arise from other-gender social comparisons.
Consistent with this perspective, lower power distance across five countries predicted
larger gender gaps in agentic and communal self-views (Guimond et al., 2007). Further,
gender gaps (favoring boys) in math performance are larger in countries lower in power
distance, suggesting that the greater self-stereotyping that arises from other-gender
comparisons can have consequences for academic outcomes (Hamamura, 2011).

The Present Research
Whereas social role theory (cf. Wood & Eagly, 2012) predicts larger gender gaps in
gendered self-views in less egalitarian countries, evolutionary approaches (cf. Schmitt, 2015)
and self-construal theorists (cf. Guimond et al., 2010) predict larger gender gaps in more
egalitarian countries. Here, we test these approaches by examining gender gaps in communal
and agentic self-views across 62 countries.
This project adds to the literature in several ways. First, the inclusion of data from 62
countries makes this the most comprehensive cross-cultural study of gendered self-views to
date; prior studies examined between 25 (Williams & Best, 1990) and 55 (Schmitt et al.,
2008) countries. Second, the recency of our data collection (2018-2020) allows for an
updated test of the universality of gender gaps in communion and agency. Third, we
examined the measurement invariance of agency and communion across world regions, thus
allowing for meaningful cross-cultural comparison of these constructs’ relations with other
variables. Note that Hsu et al.’s (2021) recent meta-analysis showed no effect of national
gender equality on gender gaps in agency, and a small positive association of national gender
equality with gender gaps in communion. However, these researchers did not demonstrate the
measurement invariance of communality and agency given their reliance on study-level data.
Fourth, we examined gender gaps as a function of both objective and subjective country-level
egalitarianism: The GGGI (World Economic Forum, 2020), which captures vertical
segregation by indexing objective gender-based disparities in access to resources and power,
and the PDI (Hofstede, 2010), which reflects subjective perceptions of general societal power
distributions.
These two measures of egalitarianism may, of course, associate differently with
gender gaps in self-views insofar as they measure different constructs: Whereas the GGGI
indexes objective outcomes that are gender-specific, the PDI indexes subjective beliefs about

power distributions in general. Thus, both social role and evolutionary theories may posit the
GGGI as a more direct predictor of women’s and men’s self-views, given these theories’
emphasis on gender as a primary source of difference. Nonetheless, the GGGI and PDI
overlap. For instance, countries higher in PDI are also higher in traditional gender ideologies
(Glick et al., 2000, 2005), and these in turn function to maintain the stability of country-level
gender hierarchies. More broadly, results of a factor analysis of 85 cultural variables showed
that GGGI and PDI both load strongly – though in opposite directions – on the same cultural
“superfactor” (Fog, 2021), reflecting cultural development and empowerment. Hence, GGGI
and PDI both reflect aspects of cultural orientations related to human development. Thus,
using both of these variables allows us to test the generalizability and consistency of our
effects across both perceived (PDI) and actual (GGGI) country-level egalitarianism.
The hypotheses listed here are pre-registered as confirmatory and exploratory (see
OSF: https://osf.io/583ct ). First, across cultures, men will rate themselves higher on agency
than women (Hypothesis 1), and women will rate themselves higher on communality than
men (Hypothesis 2). Next, we ask whether objective and subjective indices of egalitarianism
(GGGI and PDI) correlate negatively or positively with the size of gender gaps in
communality and agency (Exploratory Question 1). Because gender equality and economic
growth are bidirectionally associated (Holter, 2014; Inglehart et al, 2003), we also examine
whether patterns observed with the GGGI and PDI remain significant when controlling for
country-level wealth (Gross National Income [GNI]; United Nations Development
Programme, 2019) (Exploratory Question 2). We also controlled for age in analyses, given
different levels of variance in age across the samples.

Method
Participants and Procedure
Data were collected between January 2018 and February 2020 as part of a large cross-
cultural project (see OSF: https://osf.io/fqd4p). Participants were undergraduate students who
volunteered their time and (in most countries) received no compensation. IRB approval was
obtained at each institution when required, and all participants gave informed consent.
Participants completed a set of scales that measured more variables than those described here
(see https://osf.io/7tza3). Order of measures was randomized and data were collected via
SurveyMonkey or Qualtrics (in rare cases, participants completed paper surveys). From the
initial sample (N = 34,023), we removed records from 5,185 individuals who failed more than 1
of 3 attention checks or provided incomplete data. This yielded a final sample of N = 28,640
respondents (37% self-identified men) from 62 countries. Information on sample composition
appears in Table 1.
Measures
Bilingual scholars used the back-translation procedure (Van de Vijver & Leung, 2021) to
create 29 language versions of the surveys below.
Agency and Communality
Participants indicated the extent to which 12 agentic traits and 12 communal traits
described them on scales of 1 (does not describe me at all) to 7 (describes me well). Traits were
selected from a pool of 472 prescriptive gender stereotypes (see supplementary material, Table
S1 and https://osf.io/7tza3 ) (cf. Prentice & Carranza, 2002; Rudman et al., 2009; Williams &
Best, 1990).
Global Gender Gap Index (GGGI)
The GGGI (World Economic Forum, 2020) benchmarks women’s disadvantage,
relative to men’s, in economic, education, health, and political arenas. Thus, GGGI reflects

cross-cultural variation in vertical segregation (Wong & Charles, 2020), with scores ranging
from 0 (gender disparity) to 1 (gender parity).
Power Distance Index (PDI)
The PDI (Hofstede, 2011) measures the extent to which less powerful members of
institutions and organizations within a country expect and accept unequal power
distributions. It is measured with a scale that runs roughly from 0 to 100.
Gross National Income (GNI)
Gross National Income (GNI; United Nations Development Programme, 2019) is the
nation-level standard of living per capita adjusted for the price level of the country.
Results
Table 1 shows the country-level indicators (GGGI, PDI, and GNI) for each country.
Moreover, as detailed in the supplementary materials (see Table S2), communal and agentic
items displayed acceptable internal consistency reliabilities in all countries and the measures of
agency and communion demonstrated adequate measurement invariance across world regions. It
is therefore appropriate to compare these scores across countries. Table 2 shows mean
communality and agency scores by country, split by gender within country, and for the total
sample.
Table 1
Sample Composition and Country-Level Indicators for Each Country
Country
N
% Male
MAge
SDAge
PDI
GGGI
GNI
Albania
215
39
23.15
5.06
0.90
0.769
14 350
Argentina
345
48
32.58
12.22
0.49
0.746
22 060
Armenia
187
59
20.04
1.90
0.85
0.684
14 460
Australia
614
34
29.75
11.13
0.36
0.731
51 560
Belgium
1 681
47
21.52
5.92
0.65
0.750
54 730
Bosnia
179
49
22.95
5.75
0.90
0.712
15 770
Brazil
963
32
23.81
7.46
0.69
0.691
14 850
Canada
883
31
19.84
2.90
0.39
0.772
50 810
Chile
128
41
21.63
4.89
0.63
0.723
24 140
China
520
36
19.48
1.97
0.80
0.676
16 740
Colombia
539
39
21.49
5.05
0.67
0.758
15 150

Croatia
290
24
23.32
6.02
0.73
0.720
29 520
Czechia
365
74
27.91
8.15
0.57
0.706
40 660
Denmark
239
39
25.44
4.81
0.18
0.782
61 410
England
671
40
22.30
7.46
0.35
0.767
48 040
Finland
277
12
26.17
6.97
0.33
0.832
51 210
France
366
19
22.28
6.72
0.68
0.781
50 390
Georgia
157
53
21.83
3.33
0.65
0.708
15 020
Germany
1 257
36
29.76
10.37
0.35
0.787
57 690
Ghana
276
40
20.25
2.59
0.80
0.673
5 510
Greece
256
26
26.23
8.99
0.60
0.701
31 350
Hungary
656
18
22.36
4.25
0.46
0.677
32 750
India
332
38
22.14
5.14
0.77
0.668
6 960
Indonesia
217
47
21.02
3.96
0.78
0.700
11 930
Iran
160
40
29.21
8.31
0.58
0.584
–
Ireland
533
47
19.83
3.75
0.28
0.798
68 050
Italy
2 215
34
22.79
5.22
0.50
0.707
44 580
Japan
196
41
21.67
3.72
0.54
0.652
44 780
Kazakhstan
336
44
20.21
3.83
0.88
0.710
24 050
Kosovo
372
41
20.35
3.97
0.90
0.769
14 350
Lebanon
115
30
19.64
0.80
0.80
0.599
15 260
Lithuania
283
32
24.06
6.93
0.42
0.745
37 010
Luxembourg
174
35
24.56
5.32
0.40
0.725
77 570
Malta
235
34
26.83
9.84
0.56
0.693
41 690
Mexico
268
49
23.90
9.04
0.81
0.754
19 810
Morocco
253
46
29.28
9.55
0.70
0.605
7 680
Nepal
185
37
22.36
5.45
0.65
0.680
3 600
Netherlands
823
32
20.60
3.40
0.38
0.736
59 890
New Zealand
214
29
19.01
2.34
0.22
0.799
42 710
Nigeria
395
44
21.20
3.08
0.77
0.635
5 170
Northern Ireland
284
38
22.14
5.52
0.35
0.767
48 040
Norway
191
47
23.00
3.86
0.31
0.842
69 610
Pakistan
372
45
22.14
3.72
0.55
0.564
5 210
Philippines
417
49
19.77
2.09
0.94
0.781
10 200
Poland
729
44
22.98
4.73
0.68
0.736
32 710
Portugal
157
17
22.12
4.92
0.63
0.744
35 600
Romania
225
42
22.78
4.49
0.90
0.724
31 860
Russia
629
33
21.89
6.94
0.93
0.706
28 270
Serbia
617
25
22.12
5.14
0.86
0.736
17 960
Slovakia
516
48
21.95
4.49
1.00
0.718
33 680
South Africa
353
41
20.62
2.55
0.49
0.780
12 630
Spain
1 025
37
25.55
8.57
0.57
0.795
42 300
Suriname
153
47
22.90
5.89
0.85
0.707
15 200
Sweden
609
47
26.09
7.03
0.31
0.820
57 300
Switzerland
538
35
23.43
5.20
0.34
0.779
72 390
Turkey
1 364
32
22.28
4.06
0.66
0.635
27 410
UAE
443
35
20.00
1.34
0.80
0.655
70 240

Ukraine
258
35
19.16
1.43
0.92
0.721
13 750
Uruguay
157
40
22.71
6.70
0.61
0.737
21 120
USA
684
31
20.34
4.36
0.40
0.724
65 880
Vietnam
358
26
22.38
6.68
0.70
0.700
7 750
Wales
191
34
30.34
10.31
0.35
0.767
48 040
Total sample
28,640
37
23.05
6.82
–
–
–
Notes. PDI = Power Distance Index, GGGI = Global Gender Gap Index, GNI = Gross National Income
per capita

Table 2
Descriptive Statistics and Gender Comparision for Agency and Communality for Each Country
Country
Self-ratings on Agency
t
Cohen’s
d
Self-ratings on Communality
t
Cohen’s
d
All
Male
Female
All
Male
Female
M
SD
M
SD
M
SD
M
SD
M
SD
M
SD
Albania
5.19
0.93
5.35
0.95
5.08
0.91
2.11*
0.30
5.48
0.97
5.00
1.11
5.78
0.73
-5.69**
0.87
Argentina
4.84
0.97
4.87
0.93
4.82
1.01
0.43
0.05
5.12
0.90
5.00
0.93
5.23
0.85
-2.41*
0.26
Armenia
5.08
0.95
5.16
1.04
4.98
0.81
1.30
0.19
5.17
0.95
5.02
1.02
5.39
0.79
-2.82**
0.40
Australia
4.99
0.89
5.02
0.98
4.98
0.85
0.51
0.05
5.52
0.82
5.24
0.87
5.66
0.76
-5.85**
0.52
Belgium
4.71
0.82
4.82
0.83
4.62
0.80
4.91**
0.24
5.26
0.79
5.09
0.83
5.41
0.73
-8.59**
0.42
Bosnia
5.08
0.91
5.38
0.78
4.78
0.93
4.66**
0.70
5.50
0.76
5.37
0.69
5.64
0.81
-2.39*
0.36
Brazil
4.88
0.97
4.98
0.92
4.83
0.99
2.22*
0.15
5.23
0.81
5.03
0.78
5.33
0.80
-5.46**
0.37
Canada
4.95
0.92
5.10
0.97
4.89
0.88
3.02**
0.23
5.44
0.88
5.22
0.90
5.55
0.85
-5.12**
0.38
Chile
5.12
1.01
5.03
0.98
5.18
1.03
-0.79
0.14
5.50
1.03
5.35
0.90
5.61
1.11
-1.46
0.25
China
4.41
0.92
4.54
1.04
4.33
0.83
2.35*
0.23
5.10
0.79
4.98
0.88
5.17
0.72
-2.57**
0.25
Colombia
4.91
0.98
4.98
1.04
4.86
0.93
1.32
0.12
5.12
0.90
5.01
0.87
5.19
0.91
-2.33*
0.20
Croatia
4.83
0.92
5.06
0.99
4.76
0.88
2.19*
0.32
5.67
0.71
5.37
0.71
5.77
0.68
-4.08**
0.58
Czechia
4.72
0.89
4.74
0.91
4.67
0.83
0.75
0.09
5.13
0.82
4.99
0.80
5.52
0.74
-5.95**
0.69
Denmark
4.97
0.76
5.07
0.60
4.91
0.84
1.74
0.22
5.28
0.95
4.62
0.95
5.71
0.67
-9.70**
1.39
England
4.76
0.86
4.83
0.90
4.72
0.83
1.56
0.12
5.38
0.79
5.12
0.85
5.56
0.70
-7.04**
0.58
Finland
4.66
0.94
4.55
1.00
4.67
0.93
-0.67
0.13
5.17
0.83
4.57
0.99
5.26
0.78
-3.81**
0.85
France
4.52
0.87
4.61
0.82
4.49
0.88
1.00
0.13
5.44
0.79
5.10
0.82
5.52
0.76
-3.84**
0.54
Georgia
4.91
1.05
4.85
1.02
4.98
1.08
-0.79
0.13
5.41
0.99
5.05
1.03
5.81
0.77
-5.21**
0.82
Germany
4.82
0.84
4.83
0.83
4.81
0.84
0.30
0.02
5.30
0.79
5.05
0.78
5.43
0.77
-8.54**
0.49
Ghana
5.50
1.04
5.60
1.00
5.44
1.06
1.27
0.16
5.78
0.85
5.60
0.79
5.90
0.87
-2.96**
0.36
Greece
4.85
0.94
4.93
0.84
4.83
0.98
0.82
0.11
5.73
0.75
5.34
0.80
5.86
0.69
-4.71**
0.72
Hungary
4.70
0.91
4.71
0.95
4.70
0.90
0.08
0.01
5.50
0.81
5.12
0.93
5.58
0.76
-5.02**
0.58
India
5.42
0.85
5.47
0.84
5.40
0.86
0.76
0.09
5.69
0.74
5.52
0.72
5.79
0.74
-3.34**
0.38
Indonesia
5.09
0.86
5.17
0.89
5.01
0.83
1.39
0.19
5.55
0.69
5.62
0.69
5.49
0.69
1.36
0.19
Iran
4.71
1.00
4.92
1.07
4.57
0.93
2.11*
0.35
5.37
0.84
5.31
0.82
5.42
0.85
-0.80
0.13
Ireland
5.03
0.88
5.12
0.91
4.96
0.85
2.04*
0.18
5.18
0.80
4.98
0.79
5.36
0.76
-5.54**
0.48
Italy
4.75
0.93
4.81
0.93
4.72
0.94
2.25*
0.10
5.30
0.83
5.08
0.86
5.41
0.79
-8.89**
0.41
Japan
3.54
1.05
3.59
1.04
3.50
1.05
0.64
0.09
4.76
0.82
4.74
0.87
4.78
0.80
-0.33
0.05
Kazakhstan
4.75
0.99
4.84
0.96
4.68
1.02
1.52
0.17
5.28
0.87
5.07
0.85
5.44
0.85
-3.90**
0.43
Kosovo
5.35
0.99
5.52
0.88
5.24
1.05
2.74**
0.28
5.69
0.82
5.54
0.86
5.80
0.77
-3.04**
0.33
Lebanon
5.14
0.86
5.26
0.69
5.09
0.92
1.09
0.20
5.66
0.84
5.42
1.03
5.76
0.73
-1.75
0.41

Lithuania
4.51
0.98
4.47
1.00
4.53
0.98
-0.51
0.07
5.24
0.87
4.79
0.83
5.46
0.80
-6.37**
0.82
Luxembourg
5.20
0.83
5.28
0.83
5.15
0.83
1.00
0.16
5.57
0.73
5.40
0.77
5.66
0.69
-2.20*
0.36
Malta
5.03
0.91
5.01
1.05
5.05
0.83
-0.23
0.03
5.56
0.81
5.39
0.89
5.64
0.75
-2.16*
0.31
Mexico
5.24
0.89
5.48
0.82
5.02
0.89
4.38**
0.54
5.49
0.79
5.41
0.74
5.57
0.82
-1.65
0.20
Morocco
5.72
1.15
5.82
1.19
5.63
1.12
1.34
0.17
5.75
0.99
5.58
1.10
5.90
0.86
-2.51**
0.32
Nepal
4.88
1.04
5.00
1.07
4.81
1.02
1.18
0.18
5.50
0.84
5.33
0.89
5.59
0.80
-2.02*
0.32
Netherlands
4.72
0.73
4.83
0.78
4.67
0.70
2.72**
0.21
5.38
0.67
5.19
0.66
5.47
0.66
-5.75**
0.43
New Zealand
4.96
0.85
5.04
0.78
4.93
0.87
0.89
0.13
5.57
0.78
5.30
0.81
5.68
0.75
-3.24**
0.50
Nigeria
5.59
1.00
5.63
0.97
5.56
1.03
0.70
0.07
5.80
0.95
5.73
0.93
5.86
0.96
-1.36
0.14
Northern Ireland
4.89
0.93
5.00
1.00
4.83
0.88
1.44
0.18
5.42
0.89
4.98
0.90
5.70
0.76
-6.94**
0.88
Norway
4.64
0.78
4.79
0.77
4.52
0.76
2.43*
0.35
5.16
0.78
4.96
0.81
5.33
0.71
-3.35**
0.49
Pakistan
5.07
0.99
5.15
0.79
5.00
1.12
1.45
0.15
5.45
0.96
5.07
1.02
5.76
0.78
-7.21**
0.77
Philippines
5.09
0.88
5.09
0.91
5.10
0.85
-0.19
0.02
5.46
0.80
5.39
0.84
5.53
0.74
-1.80
0.18
Poland
4.66
0.90
4.82
0.91
4.53
0.88
4.43**
0.33
5.21
0.85
5.04
0.87
5.34
0.81
-4.79**
0.36
Portugal
4.96
0.81
5.27
0.84
4.90
0.80
2.11*
0.46
5.47
0.67
5.22
0.60
5.52
0.67
-2.37*
0.46
Romania
5.33
0.89
5.39
0.86
5.28
0.91
0.85
0.11
5.61
0.78
5.38
0.81
5.77
0.72
-3.72**
0.51
Russia
4.44
0.97
4.62
1.00
4.36
0.95
3.07**
0.27
5.24
0.82
5.01
0.85
5.35
0.79
-4.80**
0.42
Serbia
5.09
1.01
5.19
0.94
5.06
1.03
1.47
0.13
5.59
0.91
5.12
0.87
5.74
0.87
-7.68**
0.71
Slovakia
4.62
1.03
4.71
1.03
4.53
1.02
1.98*
0.17
5.24
0.89
5.04
0.86
5.42
0.88
-5.07**
0.45
South Africa
5.20
0.90
5.25
0.97
5.17
0.84
0.79
0.09
5.41
0.87
5.18
0.80
5.56
0.88
-4.19**
0.45
Spain
4.88
0.87
4.92
0.84
4.86
0.89
1.11
0.07
5.32
0.75
5.11
0.75
5.44
0.73
-6.97**
0.46
Suriname
4.93
0.95
4.93
0.81
4.92
1.06
0.01
0.00
5.54
0.79
5.32
0.86
5.73
0.68
-3.19**
0.53
Sweden
4.81
0.84
4.76
0.85
4.86
0.83
-1.50
0.12
5.16
0.79
4.91
0.80
5.39
0.71
-7.81**
0.64
Switzerland
4.83
0.83
4.89
0.88
4.80
0.81
1.17
0.11
5.39
0.76
5.12
0.78
5.54
0.71
-6.15**
0.58
Turkey
4.75
1.06
4.99
1.01
4.63
1.06
6.07**
0.35
5.47
0.80
5.36
0.83
5.51
0.78
-3.17**
0.19
UAE
4.94
0.96
5.01
0.92
4.90
0.98
1.21
0.12
5.44
0.83
5.23
0.76
5.55
0.84
-4.00**
0.39
Ukraine
4.86
0.87
5.07
0.89
4.75
0.85
2.75**
0.37
4.94
0.84
4.74
0.89
5.04
0.80
-2.73**
0.37
Uruguay
4.82
0.92
4.98
0.95
4.71
0.88
1.74
0.29
5.47
0.72
5.26
0.77
5.61
0.65
-2.93**
0.50
USA
5.05
0.94
5.13
0.89
5.02
0.96
1.52
0.12
5.48
0.87
5.23
0.84
5.59
0.86
-5.19**
0.43
Vietnam
4.32
1.01
4.49
0.96
4.26
1.02
1.97
0.23
5.29
0.79
5.17
0.79
5.33
0.79
-1.66
0.20
Wales
4.86
1.01
4.83
1.13
4.88
0.95
-0.26
0.04
5.35
1.04
4.85
1.06
5.61
0.93
-4.89**
0.78
Total sample
4.86
0.96
4.95
0.96
4.80
0.95
13.12**
0.20
5.37
0.84
5.14
0.86
5.50
0.80
-34.53**
0.43
Notes. ** p < 0.01, * p < 0.05

Primary Analyses
Given that the measures of agency and communion demonstrated adequate
measurement invariance, multilevel modelling (MLM) is appropriate. We thus used MLM to
test eight models predicting agency self-views (Models 1A-8A) and eight models predicting
communion self-views (Models 1C-8C; see Table 3). Models 1A and 1C were baseline
models with no predictors, used to calculate intraclass correlations (ICCs). Models 2A and
2C included individual-level variables (gender and age), and Models 3A, 3C, 4A, and 4C
included country-level variables as separate predictors (GGGI in 3A and 3C, and PDI in 4A
and 4C). Next, we included cross-level interaction effects of Gender-by-GGGI (see Models
5A and 5C) and Gender-by-PDI (see Models 6A and 6C). In Models 7A and 7C, we included
both of the cross-level interaction effects simultaneously to examine their unique effects, and
in Models 8A and 8C we added GNI as a covariate. In all models, we included random slopes
for gender. We used the lavaan (Rosseel, 2012) and lme4 (Bates et al., 2015) packages in the
R environment (R Core Team, 2020). Table 4 displays the fit indices for all models.

Table 3
Multilevel Models Predicting Agency and Communality Self-Views
Model type
Predictor
Self-views on Agency
Self-views on Communality
Model
1A
Model
2A
Model
3A
Model
4A
Model
5A
Model
6A
Model
7A
Model
8A
Model
1C
Model
2C
Model
3C
Model
4C
Model
5C
Model
6C
Model
7C
Model
8C
Baseline
Intercept
4.91**
4.52**
4.96**
4.33**
4.79**
4.35**
4.33**
6.15**
5.41**
5.46**
6.24**
5.30**
5.96**
5.39**
5.88**
6.64**
Individual-
level variables
(L1)
Age
–
0.01**
0.01**
0.01**
0.01**
0.01**
0.01**
0.01**
–
0.01**
0.01**
0.01**
0.01**
0.01**
0.01**
0.01**
Gender (male)
–
0.13**
0.13**
0.13**
0.54**
0.01
0.22
0.15
–
-0.37**
-0.37**
-0.37**
0.28
-0.56**
-0.16
-0.29
Country-level
variables (L2)
GGGI
–
–
-0.61
–
-0.38
–
0.03
0.71
–
–
-1.08*
–
-0.69
–
-0.63
-0.45
PDI
–
–
–
0.41*
-
0.26
0.27
-0.10
–
–
–
0.26*
-
0.11
0.04
-0.11
Log (GNI per capita)
–
–
–
–
–
–
–
-0.47**
–
–
–
–
–
–
–
-0.18
Cross-levels
interaction
component
Gender x GGGI
–
–
–
-
-0.57*
–
-0.28
-0.21
–
–
–
-
-0.90*
–
-0.50
-0.35
Gender x PDI
–
–
–
–
–
0.23**
0.20**
0.21**
–
–
–
–
–
0.31**
0.23*
0.27*
Random
effects
Residual
0.92
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.81
0.81
0.81
0.81
0.81
0.81
0.81
0.80
Gender random slope
0.16
0.07
0.07
0.07
0.06
0.06
0.06
0.06
0.41
0.16
0.16
0.16
0.15
0.15
0.15
0.14
Intercept
0.32
0.32
0.32
0.32
0.32
0.31
0.31
0.29
0.24
0.20
0.20
0.20
0.20
0.20
0.20
0.19
Notes. Number of observations = 28,640; Number of countries = 62. Models 7A/C and 8A/C were tested on 61 countries and 28,480 observations. * p < 0.05; ** p < 0.01.

Table 4
Multilevel Models’ Fit Indices
Model
Type
Description
Self-views on Agency (Models A)
Self-views on Communality (Models C)
Δ df
- 2 log
likelihood
AIC
L. Ratio
Δ df
- 2 log
likelihood
AIC
L. Ratio
1A/C
Baseline
Individuals nested within their country with no other predictors
–
76729
76739
–
–
69253
69263
–
2A/C
Random
coefficient and
fixed
predictors
Individual-level variables: Age and Gender
2
76402
76416
327**
2
69138
69152
116**
3A/C
Individual and country level variables: Age, Gender, GGGI
0
76401
76417
1
0
69132
69148
6*
4A/C
Individual and country level variables: Age, Gender, PDI
0
76399
76414
4*
0
69134
69150
4*
5A/C
Individual (Age, Gender) and country level (GGGI) variables and cross-
levels interaction (Gender x GGGI)
1
76397
76415
4*
1
69127
69145
5*
6A/C
Individual (Age, Gender) and country level (PDII) variables and cross-
levels interaction (Gender x PDI)
1
76388
76406
10*
1
69125
69143
8*
7A/C
Individual (Age, Gender) and country level (GGGI, PDI) variables and
cross-levels interactions (Gender x GGGI, Gender x PDI)
1
76387
76409
1
1
69120
69142
5*
8A/C
Individual (Age, Gender) and country level (GGGI, PDI, GNI per capita)
variables and cross-levels interactions (Gender x GGGI, Gender x PDI)
–
75929
75953
– a
–
68718
68742
– a
Notes. AIC = Akaike’s information criteria; GGGI = Global Gender Gap Index; PDI = Power Distance Index; GNI = Gross National Income per capita. Models 8A/C were
tested on 61 countries and 28,480 observations, p < 0.05; ** p < 0.01.

Gendered Self-Views Across 62 Countries
Sex Differences in Agentic Self-Views
In Model 1A, 11% of the variance in agency was explained by country (ICC = 0.11),
indicating a multilevel approach was appropriate (Dyera et al., 2005). Next, in support of
Hypothesis 1, there was a main effect of gender such that men described themselves as more
agentic than women (see Tables 3 and 4, Model 2A). However, analyses of gender gaps in agency
by country (see Table 2) yielded significant differences in only 20 out of 62 (32%) countries.
Moreover, the whole sample effect size was small (d = .20). Thus, we found partial support for
Hypothesis 1.
Models 5A and 6A tested Exploratory Question 1 by examining interactions of gender
with GGGI and PDI predicting agentic self-views. First, as shown in Tables 3 and 4 (see
Model 5A) and illustrated in Figure 1
1
, the Gender-by-GGGI interaction was significant such
that gender gaps in agency were smaller in countries higher in GGGI. This pattern was driven
primarily by men: We found insufficient evidence to indicate that women’s agency differed
by GGGI (B = 0.19, p = 0.15), whereas men reported significantly lower agency in countries
higher in GGGI (B = -0.64, p < 0.01). Similarly, the Gender-by-PDI interaction was
significant (see Tables 4 and 5, Model 6A). As shown in Figure 2, gender gaps in agency were
smaller in countries lower in PDI, and again, the pattern was driven more by men than
women: We found no evidence that women’s agency differed by PDI (B = -0.001, p = 0.94),
while men reported significantly lower agency in countries lower in PDI (B = 0.27, p < 0.01).
Thus, on both objective and subjective country-level indices, gender gaps in agentic self-
views were smaller when egalitarianism was higher. These patterns are consistent with social
role theory’s assumption that reductions in vertical segregation should lead to greater
similarity of women’s and men’s agentic self-views.
1
See the supplementary materials for Figures S1-S4, which illustrate women’s and men’s average agentic and
communal self-views, with countries ordered from low to high in GGGI and PDI.

Gendered Self-Views Across 62 Countries
Figure 1
GGGI Predicts Country-Level Binary Gender Gaps in Agentic Self-Views
Note. Dots are mean raw agency self-views for each gender in each country. Lines are simple
regression lines.

Gendered Self-Views Across 62 Countries
Figure 2
PDI Predicts Country-Level Binary Gender Gaps in Agentic Self-Views
Note. Dots are mean raw agency self-views for each gender in each country. Lines are simple
regression lines.

Gendered Self-Views Across 62 Countries
When we included both of the cross-level interaction effects simultaneously to
examine their unique effects (Model 7A), the Gender-by-GGGI interaction was no longer
significant but the Gender-by-PDI interaction remained significant (see Table 3). The Gender-
by-PDI interaction also remained significant when we added GNI as a covariate (Model 8A).
Sex Differences in Communal Self-Views
In Model 1C, 5% of the variance in communality was explained by country (ICC =
0.05), indicating that a multilevel approach was suitable. Strongly supporting Hypothesis 2, a
main effect of gender emerged (see Tables 3 and 4, Model 2C). Women described themselves
as more communal than men in 53 of 62 (85%) countries, with a medium whole sample effect
size of d = .43 (see Table 2).
Exploratory Question 1 was tested in Models 5C and 6C via interactions of gender
with GGGI and PDI predicting communal self-views. As shown in Tables 3 and 4 (Model 5C)
and illustrated in Figure 3, there was a significant Gender-by-GGGI interaction. Gender gaps
in communality were larger in countries higher in GGGI, driven by a (weaker) negative
association of women’s communality (B = -0.42, p < 0.01), and by a (stronger) negative
association of men’s communality (B = -1.23, p < 0.01), with country-level GGGI. Similarly,
the Gender-by-PDI interaction was significant (see Tables 3 and 4, Model 6C). As illustrated
in Figure 4, gender gaps in communality were larger in countries lower in PDI, and this
pattern was driven by men: We found no evidence that women’s communality differed by
PDI (B = 0.002, p = 0.93), whereas men reported significantly lower communality in
countries lower in PDI (B = 0.34, p < 0.01). Thus, on both objective and subjective country-
level indices, gender gaps in communal self-views were larger when cultural egalitarianism
was higher. These patterns are consistent with the evolutionary and self-construal approaches.

Gendered Self-Views Across 62 Countries
Figure 3
GGGI Predicts Country-Level Binary Gender Gaps in Communal Self-Views
Note. Dots are mean raw communality self-views for each gender in each country. Lines are
simple regression lines.

Gendered Self-Views Across 62 Countries
Figure 4
PDI Predicts Country-Level Binary Gender Gaps in Communal Self-Views
Note. Dots are mean raw communality self-views for each gender in each country. Lines are
simple regression lines.

Gendered Self-Views Across 62 Countries
When we included both of the cross-level interaction effects simultaneously to
examine their unique effects (Model 7C), the Gender-by-GGGI interaction became non-
significant but the Gender-by-PDI interaction remained significant (see Table 3). The Gender-
by-PDI interaction also remained significant when we added GNI as a covariate in Model 8C.
Discussion
Across 62 countries, we examined the universality of gendered self-views, and tested
two models of the links between gender gaps in gendered self-views and country-level
egalitarianism. Consistent with our expectations and past cross-cultural investigations (e.g.,
Williams & Best, 1990), women all over the world view themselves higher in communality
than men. Men, conversely, view themselves higher in agency than women. However, this
latter sex difference is less consistent across countries than is the sex difference in communal
self-views. Thus, whereas women’s greater self-perceived communality is universal, men’s
greater agency is a much more variable phenomenon. Given the limited movement of men
into domestic and caregiving roles, and the continued predominance of women in these
communal activities (Croft et al., 2015), women clearly still view themselves as more
communal than men.
Next, using both objective (GGGI) and subjective (PDI) indices, we examined the size
of gender gaps in agentic and communal self-views as a function of country-level
egalitarianism. Here, we found that gender gaps in agency were smaller, whereas gender gaps
in communality were larger, in countries higher in gender equality and lower in power
distance. These patterns emerged consistently across both the GGGI and PDI in models that
examined these country-level predictors separately. However, in models that entered both
country-level predictors simultaneously, only subjective egalitarianism (PDI) uniquely
predicted gender gaps in gendered self-views. That is, we found no evidence that GGGI
interacted with gender to predict self-views when PDI was in the model. This suggests that

Gendered Self-Views Across 62 Countries
objective gender equality’s shared variance with PDI accounts for its associations with self-
views in our analyses, a finding that bears further scrutiny. In contrast, subjective perceptions
of power distance capture something that goes beyond both objective gender equality and
wealth.
How can we explain the seemingly contradictory tendency for more egalitarian
countries to be associated with smaller gender gaps in agency and larger gender gaps in
communality? On one hand, these patterns may be explained by social role theory (cf. Wood
& Eagly, 2012), if we consider how self-views are shaped by both vertical and horizontal
gender inequality (only the former of which was measured here). Eagly and colleagues (2020)
found that stereotypes regarding women’s communality advantage increased in the U.S. from
1946 to 2018, while stereotypes regarding men’s agency advantage declined weakly and non-
significantly. To explain this, Eagly et al. suggested that reductions in vertical segregation
decreased men’s agency advantage as U.S. women increasingly entered high-status and
leadership positions over time. Concurrently, women’s communality advantage increased due
to women’s continued overrepresentation in domestic roles, combined with increasing levels
of horizontal gender segregation as women concentrated into female-dominated occupational
subfields such as education or health care (Charles & Bradley, 2009).
Applying this logic to the current findings, perhaps gender gaps in agency decline with
country-level differences in PDI (which indexes vertical segregation), while sex differences in
communality increase with country-level differences in horizontal segregation. Even in the
most egalitarian countries, domestic roles remain markedly gender segregated, with women
doing most of this work regardless of whether they work outside the home (Croft et al., 2015;
Kan et al., 2011). And these gender disparities in domestic responsibilities may be especially
salient in more egalitarian countries, as they challenge expectations of equality. Moreover,
countries higher in egalitarianism may, curiously, be higher in horizontal segregation (Jarman

Gendered Self-Views Across 62 Countries
et al., 1999). If so, this may help explain the larger gender gaps in communal self-views
observed in more egalitarian countries. Note that in Hsu et al.’s (2021) meta-analysis of
gender gaps in agency and communion, they found a weak tendency for national gender
equality to predict a larger gender gap in communion (as we did here), but this effect was no
longer significant when they controlled for horizontal segregation in a small subset of
countries. Instead, only horizontal segregation uniqely predicted gender gaps in communion.
Thus, it is plausible that different types of segregation predict gender gaps in agency versus
communion. Unfortunately, a strong test of this hypothesis requires a cross-culturally
validated measure of horizontal segregation, which to our knowledge does not exist. Another
issue that must await future tests was our finding that gender gaps in agency and communion
across countries were driven primarily by men’s self-views, a pattern which is inconsistent
with social role theory.
On the other hand, proponents of the evolutionary approach would argue that our
findings for communality – i.e., larger gender gaps in more egalitarian, lower power distance
countries – are consistent with assumptions about evolved adaptations that are more freely
expressed in more developed countries (Schmitt et al., 2008). These communality findings
also add to the Gender Equality Paradox (GEP; Connolly et al., 2020; Stoet & Geary, 2019)
literature, which is typically explained with evolutionary logic. Moreover, as noted above, we
found that gender gaps for both self-view dimensions were driven more strongly by variations
in men’s than women’s self-views: Whereas we found little evidence that women’s communal
and agentic self-views differed across countries as a function of egalitarianism, men view
themselves both as less agentic and as less communal in more egalitarian countries. These
patterns are consistent with the evolutionary approach which assumes that, in sexually
dimorphic species, the larger sex is more vulnerable to environmental pressures (Abouheif &
Fairbairn, 1997), and thus variations in men’s traits should drive variations in sex differences

Gendered Self-Views Across 62 Countries
across cultures (Schmitt et al., 2008). However, the evolutionary approach cannot easily
explain our findings regarding agency.
Similarly, proponents of self-construal approaches would explain our communality
findings as reflecting cross-country differences in people’s reliance on other-gender social
comparisons when describing themselves (e.g., Guimond et al., 2007). In countries lower in
power distance, in which individuals make more other-gender social comparisons, we see
larger gender gaps in communal self-views. Other-gender social comparisons should amplify
gender gaps in gendered self-views by highlighting group boundaries and eliciting self-
stereotyping. Of course, this approach also cannot explain our findings regarding agency, nor
why PDI predicts men’s self-views across countries and not women’s. Moreover, self-
construal approaches do not offer insights into why agency and communion are relevant to
gender in the first place.
Finally, cultural differences in core values provide another possible explanation for
our communality findings. People generally attribute the most culturally valued traits to more
dominant social groups, which are usually men (Sidanius & Pratto, 1999). Thus, stereotypes
about men tend to differ with the core values of a given culture. For example, men are
stereotyped and prescribed as more communal in less egalitarian (low GGGI, high PDI)
countries (e.g., Cuddy et al., 2015), presumably because such cultures value communal
qualities that promote interdependence. Using similar logic, men in less egalitarian countries
likely develop more communal self-views as they internalize prescriptive, communal
stereotypes. This perspective can help explain why men, in particular, exhibit more communal
self-views in less egalitarian countries where these traits are highly valued. At the same time,
the cultural values perspective – like the evolutionary and self-construal perspectives – cannot
explain why men in more egalitarian countries exhibit less agentic self-views. Agency is more
valued in more egalitarian (and richer) countries (Sedikides et al., 2003), and we thus would

Gendered Self-Views Across 62 Countries
expect people to internalize this socially desired trait. That men instead report less agentic
self-views in more egalitarian countries thus remains an open question in need of more
research.
Limitations and Future Research
Our dataset covers a large multi-country sample but our participants were all
university students and we did not measure their employment status. Moreover, most of the
samples did not have sufficient variance in age to allow us to examine whether our findings
were moderated by age. We caution readers not to generalize our findings to all or most
residents of the countries we studied.
As noted earlier, future studies should continue to explore the joint and unique
predictive utility of distinct indicators of country-level egalitarianism. Most societies are
structured by a gendered division of labor that mirrors prescriptive and proscriptive gender
roles, which both create and reinforce gender hierarchies (Eagly & Wood, 1999). PDI and
GGGI both reflect and promote social inequalities and correlate with country-level wealth
(GGGI-GNI: r = 0.50; PDI-GNI: r = -0.63) but our results demonstrate that only PDI, and not
GGGI, significantly predicts gendered self-views when both of these indices are included in
analyses. This suggests that country-level, objective gender equality is not directly linked to
gendered self-views, but may instead operate through proximal, subjective perceptions of
inequality. Perhaps this is because GGGI reflects objective, structural outcomes related to
gender that operate more distally, while PDI reflects internalized, subjective perceptions of
gender (and other social) hierarchies. Recall also that PDI and GGGI similarly reflect
fundamental elements of cultural orientations related to human development (Fog, 2021).
Finally, recall that Hsu et al. (2021) found that the association of GGGI with gender gaps in
communal self-views became non-significant when controlling for horizontal segregation.
Thus, our findings join a growing body of research indicating that GGGI itself may not be a

Gendered Self-Views Across 62 Countries
primary or direct diver of gender gaps in self-views. It is difficult to disentangle the effects of
objective gender equality from other aspects of egalitarianism and human development,
highlighing the need for a nuanced framework specifying precisely if and how objective
gender equality directly and/or indirectly influences gendered self-views (cf. Connolly et al.,
2020).
Finally, future research should seek to replicate our self-view findings using measures
of gender stereotypes of agency and communion. It will be important to examine whether
cross-cultural gender stereotypes map closely onto people’s gendered self-views, as several
theoretical perspectives would predict (Tobin et al., 2010; Turner et al., 1987; Wood & Eagly,
2012).
Conclusions
Social role theory predicts that gender gaps should shrink as societies become less
vertically gender segregated. Conversely, evolutionary and self-construal theories anticipate
larger gender gaps in more egalitarian countries (Guimond et al., 2007; Schmitt, 2015). Here,
results from a large, 62-country dataset, show that gender gaps in gendered self-views
correlate differently with cultural egalitarianism depending on the dimension (and the
egalitarianism index) under examination: Gender gaps in agentic self-views are smaller, and
gender gaps in communal self-views are larger, in more egalitarian countries. These patterns
emerged across two distinct, objective and subjective country-level indices of egalitarianism,
but are accounted for more robustly by subjective than objective egalitarianism. Moreover,
whereas women’s more communal self-views appear universal, men’s more agentic self-
views vary considerably across countries, and cross-country patterns were driven more by
variations in men’s than women’s self-views. We encourage future research to examine cross-
country gender gaps in gendered self-views through the lens of culturally constructed gender

Gendered Self-Views Across 62 Countries
identities (Charles & Bradley, 2009), and to seek evidence of explanatory mechanisms that
can explain the associations between country-level predictors and individuals’ self-views.

Gendered Self-Views Across 62 Countries
References
Abouheif, E., & Fairbairn, D. J. (1997). A comparative analysis of allometry for sexual size
dimorphism: assessing Rensch's rule. The American Naturalist, 149(3), 540-562.
https://doi.org/10.1086/286004
Bakan, D. (1966). The duality of human existence: An essay on psychology and religion.
Rand McNally.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models
using lme4. Journal of Statistical Software, 67.https://doi.org/10.18637/jss.v067.i01
Biernat, M., Vescio, T. K., & Green, M. L. (1996). Selective self-stereotyping. Journal of
Personality and Social Psychology, 71(6), 1194–1209. https://doi.org/10.1037/0022-
3514.71.6.1194
Bosson, J. K., & Michniewicz, K. S. (2013). Gender dichotomization at the level of ingroup
identity: What it is, and why men use it more than women. Journal of Personality and
Social Psychology, 105(3), 425-442. https://doi.org/10.1037/a0033126
Breda, T., Jouini, E., Napp, C., & Thebault, G. (2020). Gender stereotypes can explain the
gender-equality paradox. Proceedings of the National Academy of Sciences, 117(49),
31063-31069. https://doi.org/10.1073/pnas.2008704117
Buss, D. M., & Schmitt, D. P. (1993). Sexual Strategies Theory: An evolutionary perspective on
human mating. Psychological Review, 100(2), 204–232. https://doi.org/10.1037/0033-
295x.100.2.204
Charles, M. (1992). Cross-national Variation in Occupational Sex Segregation. American
Sociological Review, 57, 483–502. https://doi.org/10.2307/2096096
Charles, M., & Bradley, K. (2009). Indulging our gendered selves? Sex segregation by field of
study in 44 countries. American Journal of Sociology, 114(4), 924-976.
https://doi.org/10.1086/595942

Gendered Self-Views Across 62 Countries
Charmes, J. (2019). The Unpaid Care Work and the Labour Market. An analysis of time use
Office. Retrieved from: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---
gender/documents/publication/wcms_732791.pdf
Chazal, S., Guimond, S., & Darnon, C. (2012). Personal self and collective self: When
academic choices depend on the context of social comparison. Social Psychology of
Education, 15(4), 449-463. https://doi.org/10.1007/s11218-012-9199-x
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance.
Structural Equation Modeling, 14(3), 464–504.
https://doi.org/10.1080/10705510701301834
Connolly, F., Goossen, M. & Hjerm, M. (2020). Does Gender Equality Cause Gender
Differences in Values? Reassessing the Gender-Equality-Personality Paradox. Sex
Roles 83, 101–113. https://doi.org/10.1007/s11199-019-01097-x Costa, P. T.,
Terracciano, A., & McCrae, R. R. (2001). Gender differences in personality traits
across cultures: Robust and surprising findings. Journal of Personality and Social
Psychology, 81(2), 322–331. https://doi.org/10.1037/0022-3514.81.2.322
Crawford, C. (1998). Environments and adaptations: Then and now. In C. Crawford & D. L.
Krebs (Eds.), Handbook of evolutionary psychology: Ideas, issues, and applications
(pp. 275–302). Mahwah, NJ: Erlbaum.
Croft, A., Schmader, T., & Block, K. (2015). An underexamined inequality: Cultural and
psychological barriers to men’s engagement with communal roles. Personality and
Social Psychology Review, 19(4), 343-370.
https://doi.org/10.1177/1088868314564789
Cuddy, A. J., Wolf, E. B., Glick, P., Crotty, S., Chong, J., & Norton, M. I. (2015). Men as
cultural ideals: Cultural values moderate gender stereotype content. Journal of

Gendered Self-Views Across 62 Countries
Personality and Social Psychology, 109(4), 622-635.
https://doi.org/10.1037/pspi0000027
Donnelly, K., & Twenge, J. M. (2017). Masculine and feminine traits on the Bem Sex-Role
Inventory, 1993–2012: A cross-temporal meta-analysis. Sex Roles, 76(9), 556-565.
https://doi.org/10.1007/s11199-016-0625-y
Dyer, N. G., Hanges, P. J., & Hall, R. J. (2005). Applying multilevel confirmatory factor
analysis techniques to the study of leadership. Leadership Quarterly, 16(1), 149-167.
https://doi.org/10.1016/j.leaqua.2004.09.009
Eagly, A. H. (1987). Sex differences in social behavior: A social-role interpretation. Lawrence
Erlbaum Associates, Inc.
Eagly, A. H., Nater, C., Miller, D. I., Kaufmann, M., & Sczesny, S. (2020). Gender stereotypes
have changed: A cross-temporal meta-analysis of U.S. public opinion polls from 1946 to
2018. American Psychologist, 75(3), 301–315. https://doi.org/10.1037/amp0000494
Eagly, A. H., & Steffen, V. J. (1984). Gender stereotypes stem from the distribution of women
and men into social roles. Journal of Personality and Social Psychology, 46(4), 735–754.
https://doi.org/10.1037/0022-3514.46.4.735
Eagly, A. H., & Wood, W. (1999). The origins of sex differences in human behavior: Evolved
dispositions versus social roles. American Psychologist, 54(6), 408–423.
https://doi.org/10.1037/0003-066X.54.6.408
Eagly, A. H., Wood, W., & Diekman, A. B. (2000). Social role theory of sex differences and
similarities: A current appraisal. In T. Eckes & H. M. Trautner (Eds.), The developmental
social psychology of gender (p. 123–174). Lawrence Erlbaum Associates Publishers.
Falk, A., & Hermle, J. (2018). Relationship of gender differences in preferences to economic
development and gender equality. Science. 362(6412), eaas9899.
https://doi.org/10.1126/science.aas9899

Gendered Self-Views Across 62 Countries
Fiske, S. T., Cuddy, A. J., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype
content: Competence and warmth respectively follow from perceived status and
competition. Journal of Personality and Social Psychology, 82(6), 878-902.
https://doi.org/10.1037//0022-3514.82.6.878
Fog, A. (2021). A Test of the Reproducibility of the Clustering of Cultural Variables. Cross-
Cultural Research, 55(1), 29–57. https://doi.org/10.1177/1069397120956948
Glick, P., Fiske, S. T., Mladinic, A., Saiz, J. L., Abrams, D., Masser, B., Adetoun, B., Osagie,
J. E., Akande, A., Alao, A., Annetje, B., Willemsen, T. M., Chipeta, K., Dardenne, B.,
Dijksterhuis, A., Wigboldus, D., Eckes, T., Six-Materna, I., Expósito, F., . . . López, W. L.
(2000). Beyond prejudice as simple antipathy: Hostile and benevolent sexism across
cultures. Journal of Personality and Social Psychology, 79(5), 763-775.
https://doi.org/10.1037/0022-3514.79.5.763
Grimm, S. D., & Church, A. T. (1999). A cross-cultural study of response biases in personality
measures. Journal of Research in Personality, 33(4), 415–
441. https://doi.org/10.1006/jrpe.1999.2256
Guimond, S. (2008). Psychological similarities and differences between women and men across
cultures. Social and Personality Psychology Compass, 2(4), 494–510. https://doi-
org.ezproxy.lib.usf.edu/10.1111/j.1751-9004.2007.00036.x
Guimond, S., Branscombe, N. R., Brunot, S., Buunk, A. P., Chatard, A., Désert, M., Garcia, D.
M., Haque, S., Martinot, D., & Yzerbyt, V. (2007). Culture, gender, and the self:
Variations and impact of social comparison processes. Journal of Personality and Social
Psychology, 92(6), 1118–1134. https://doi.org/10.1037/0022-3514.92.6.1118
Hamamura, T. (2012). Power distance predicts gender differences in math performance across
societies. Social Psychological and Personality Science, 3(5), 545-548.
https://doi.org/10.1177/1948550611429191

Gendered Self-Views Across 62 Countries
Holter, Ø. G. (2014). "What’s in it for men?": Old question, new data. Men and Masculinities,
17(5), 515–548. https://doi.org/10.1177/1097184x14558237
Hsu, N., Badura, K. L., Newman, D. A., & Speach, M. E. P. (2021). Gender, “masculinity,” and
“femininity”: A meta-analytic review of gender differences in agency and
communion. Psychological Bulletin, 147(10), 987 -
1011. https://doi.org/10.1037/bul0000343
Inglehart, R., Norris, P., & Ronald, I. (2003). Rising tide: Gender equality and cultural change
around the world. Cambridge University Press.
https://doi.org/10.1017/CBO9780511550362
Jarman, J. Blackburn, RM., Brooks, B., Dermott, E. (1999). Gender differences at work:
International variations in occupational segregation. Sociological Research Online 4(1).
Available at: http://www.socresonline.org.uk/4/1/jarman.html
Kan, M. Y., Sullivan, O., & Gershuny, J. (2011). Gender convergence in domestic work:
Discerning the effects of interactional and institutional barriers from large-scale
data. Sociology, 45(2), 234-251. https://doi.org/10.1177/0038038510394014
Kline, R. B. (2016). Principles and practice of structural equation modelling (4th ed.). New
York: The Guilford Press.
Konrad, A. M., Ritchie, J. E., Jr., Lieb, P., & Corrigall, E. (2000). Sex differences and similarities
in job attribute preferences: A meta-analysis. Psychological Bulletin, 126(4), 593–641.
https://doi.org/10.1037/0033-2909.126.4.593Lord F. M., & Novick M. R.
(1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
Lueptow, L. B., Garovich-Szabo, L., & Lueptow, M. B. (2001). Social change and the persistence
of sex typing: 1974–1997. Social Forces, 80(1), 1–36.
https://doi.org/10.1353/sof.2001.0077
Milfont, T. L., & Fisher, R. (2010). Testing measurement invariance across groups: Applications

Gendered Self-Views Across 62 Countries
in cross-cultural research. International Journal of Psychological Research, 3(1), 111–
121. https://doi.org/10.21500/20112084.85
Millsap, R. E. (2011). Statistical approaches to measurement invariance. Routledge/Taylor &
Francis Group.
Ng, T. W., Eby, L. T., Sorensen, K. L., & Feldman, D. C. (2005). Predictors of objective and
subjective career success: A meta‐analysis. Personnel Psychology, 58(2), 367-408.
https://doi.org/10.1111/j.1744-6570.2005.00515.x
Niedenthal, P. M., Krauth-Gruber, S., & Ric, F. (2006). Psychology of emotion: Interpersonal,
experiential, and cognitive approaches. Psychology Press.
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for
Statistical Computing. Vienna, Austria. https://www.R-project.org/
Rogoza, R., Żemojtel-Piotrowska, M., Jonason, P. K., Piotrowski, J., Campbell, K. W., Gebauer,
J. E., Maltby, J., Sedikides, C., Adamovic, M., Adams, B. G., Ang, R. P., Ardi, R.,
Atitsogbe, K. A., Baltatescu, S., Bilić, S., Bodroža, B., Gruneau Brulin, J., Bundhoo
Poonoosamy, H. Y., Chaleeraktrakoon, T., … Włodarczyk, A. (2021). Structure of dark
triad dirty dozen across eight world regions. Assessment, 28(4), 1125–1135.
https://doi.org/10.1177/1073191120922611
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical
Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
Schmitt, D. P. (2005). Sociosexuality from Argentina to Zimbabwe: A 48‐nation study of sex,
culture, and strategies of human mating. Behavioral and Brain Sciences, 28(2), 247–
275. https://doi.org/10.1017/s0140525x05000051
Schmitt, D.P. (2015). Fundamentals of human mating strategies. In D.M. Buss (Ed.). The
Handbook of Evolutionary Psychology (pp. 258-291). John Wiley & Sons.
https://doi.org/10.1002/9780470939376.ch9

Gendered Self-Views Across 62 Countries
Schmitt, D. P., Long, A. E., McPhearson, A., O'Brien, K., Remmert, B., & Shah, S. H. (2017).
Personality and gender differences in global perspective. International Journal of
Psychology, 52(S1), 45-56. https://doi.org/10.1002/ijop.12265
Schmitt, D. P., Realo, A., Voracek, M., & Alik, J. (2008). Why can’t a man be more like a
woman? Sex differences in Big Five personality traits across 55 cultures. Journal of
Personality and Social Psychology, 94(1), 168–192. https://doi.org/10.1037/a0014651.
Sedikides, C., Gaertner, L., & Toguchi, Y. (2003). Pancultural self-enhancement. Journal of
Personality and Social Psychology, 84(1), 60–79. https://doi.org/10.1037/0022-
3514.84.1.60
Sidanius, J., & Pratto, F. (1999). Social dominance: An intergroup theory of social hierarchy and
oppression. Cambridge University Press. https://doi.org/10.1017/CBO9781139175043
Stoet, G., & Geary, D. C. (2019). A simplified approach to measuring national gender
inequality. PLoS ONE, 14(1), e0205349. https://doi.org/10.1371/journal.pone.0205349
Tobin, D. D., Menon, M., Menon, M., Spatta, B. C., Hodges, E. V. E., & Perry, D. G. (2010). The
intrapsychics of gender: A model of self-socialization. Psychological Review, 117(2),
601–622. https://doi.org/10.1037/a0018936
Trivers, R. L. (1972). Parental Investment and Sexual Selection. In B. Campbell (Ed.), Sexual
Selection and the Descent of Man, 1871-1971 (pp. 136-179). Chicago, IL: Aldine.
Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987).
Rediscovering the social group: A self-categorization theory. Basil Blackwell.
United Nations Development Programme. (2019). Human development report 2019. Beyond
income, beyond averages, beyond today: inequalities in human development in the 21st
century. New York, NY. //hdr.undp.org/sites/default/files/hdr2019.pdf

Gendered Self-Views Across 62 Countries
United Nations Statistics Division. (2021). Standard country or area codes for statistical use
(M49). New York, NY. https://unstats.un.org/unsd/methodology/m49/overview/
van de Vijver, F. J. R., & Leung, K. (2021). Methods and Data Analysis for Cross-Cultural
Research (V. Fetvadijev & J. He (eds.); 2nd ed.). Cambridge University Press.
Wang, M. T., & Degol, J. L. (2017). Gender gap in science, technology, engineering, and
mathematics (STEM): Current knowledge, implications for practice, policy, and future
directions. Educational Psychology Review, 29(1), 119-140.
https://doi.org/10.1007/s10648-015-9355-x
Williams, J. E., & Best, D. L. (1990). Measuring sex stereotypes: A multination study (Rev. ed.).
Sage Publications, Inc.
Wood, W., & Eagly, A. H. (2012). Biosocial construction of sex differences and similarities in
behavior. Advances in Experimental Social Psychology, 46, 55-123.
https://doi.org/10.1016/B978-0-12-394281-4.00002-7
Wong, Y. L. A., & Charles, C. (2020). Gender and occupational segregation. In N. A. Naples
(Ed.), Companion to Women’s and Gender Studies (pp. 305-327). Wiley.
World Bank. (2020). GNI per capita, PPP (current international $). Washington, D.C.
https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD.
World Economic Forum. (2020). Global Gender Gap Report 2020. Geneva, Switzerland.
http://www3.weforum.org/docs/WEF_GGGR_2020.pdf
Zentner, M., & Mitura, K. (2012). Stepping out of the caveman’s shadow: Nations’ gender
gap predicts degree of sex differentiation in mate preferences. Psychological Science,
23(10), 1176–1185. doi:10.1177/0956797612441